Description: Profile of an actuary in a non-traditional actuarial role: focusing on medical cost and analysis, both retrospective and prospective.

Description: This paper presents a procedure for calculating aggregate stop-loss premiums that takes parameter uncertainty into account. From Actuarial Research Clearing House ARCH 1988, Vol. 3

Description: This paper presents a method for analyzing the experience of a stop-loss underwriter to develop a claim continuance table. This method is designed to deal with the limitations presented from certain limited information so as to get the most out of the available information. To the extent this approach is succesful, the credibility of the experience analysis will be maximized, thus minimizing the reliance on external data. From Actuarial Research Clearing House 1993, VOL. 3.

Description: ReMark's Global Consumer Study delivers 10 key insights to empower customers. Insurers can design products that meet customers' needs and enhance client experiences and add significant value to their business. At the same time with new products being launched seemingly continuously, it is important to take a step back and assess risk throughout the product management cycle.

Description: "Can you answer the question 'what are the chances we will lose more than $1 million next year?' without saying 'it depends'? If you miss a projection, can you quantify the chances that the miss was due to random variation vs. an incorrect projection? ten in as presenters demonstrate how to answer these questions and others using total risk analysis. Total risk analysis includes quantifying both the random variation risk and the projection risk associated with actuarial functions like pricing and reserving. After attending this session, you will be able to describe total risk analysis, apply total risk analysis to many actuarial functions."

Description: Read the February 2020 issue of Health Watch, published by the Health Section. Practical information for actuaries, articles impact sections, section activities.

Description: The National Association of Worksite Medical Clinics reported that one-third of U.S. employers with 5,000+ employees sponsored worksite (onsite) health care centers in 2017, and 24 percent of employers with 500-4,999 either have or plan to implement onsite health care by 2019. This session will explore why onsite health care programs are gaining traction: • Why do employers sponsor onsite healthcare? • What objectives do they hope to achieve? • How does the evidence compare with these objectives?

Description: On average, contracts receiving their first star rating receive 3.20 stars, compared to existing or established contracts receiving 3.90 stars on average. This 0.70 disparity in quality ratings creates significant hardship on new contracts when this lower star rating decreases their payments in the following calendar year. This article explores the disparity in Medicare Advantage Star Ratings between contracts receiving their first star ratings (“New” contracts) and existing contracts, including: 1. Understanding what happens to Medicare Advantage Payments when contracts receive lower star ratings? – What is the Star Rating cliff and why does it matter to new contracts? 2. Explaining when a contract receives their first star rating and how much enrollment this requires – When does the Star Rating cliff occur? 3. Noting which specific star measures “New” contracts struggle with, including those contracts under large parent organizations, contracts with rapid growth, and all other new contracts. – How can my contract avoid the Star Rating cliff?

Description: Submission components, format and considerations.

Description: This report provides actuaries and other professionals working with risk, data and analytics or program evaluation in the health care space with considerations and frameworks related to the use of determinants of health (DOH) in data and actuarial work streams. Such frameworks can leverage new data sources to enrich health care data analysis to move beyond traditional data elements, such as age, gender, zip code or health diagnosis.